[Disclosure Agreement Three] Practical combat: writing the first cross-platform AI ecommerce analysis gadget

Direct answer: The key to cross-platform AI ecommerce tools is to convert data from each platform into a common payload and then submit it to the AI Ready Gateway for analysis. Tools should not directly modify items, inventory, or prices, but should first generate auditable reports such as inventory risk, slow-moving items, and replenishment recommendations.

Who should read this?#

• Full-end developers who want to start AI ecommerce integration from scratch.

• Outsourcing teams that need to build reporting tools for WooCommerce, PrestaShop, OpenCart or Magento.

• Technical directors who want to know how the AI Ready protocol is implemented.

Implementation goal: Inventory level analysis report#

The gadget we want to make is very clear: read the sales summary and current inventory of the last 30 days, and generate three types of reports: "Possibly out of stock", "Possibly unsalable" and "Requires manual inspection".

This tool is suitable as a first AI Ready implementation because it has three advantages:

1. Only summary information is required, no customer personal information is required.
2. The output is a suggested report and will not directly modify the transaction data.
3. WooCommerce, PrestaShop, OpenCart, and Magento all have similar SKU, sales, and inventory concepts.

Step 1: Establish a common data format#

{
  "sku": "BAG-18L-NAVY",
  "name": "18L 防潑水通勤背包",
  "current_stock": 84,
  "sales_30d": 126,
  "returns_30d": 3,
  "supplier_lead_time_days": 21
}

Different platforms can use different methods to obtain data, but they should be organized into a common format before sending to AI Ready.

Step 2: Encapsulate AI Ready Payload#

{
  "intent": "analyze_inventory_risk",
  "source": {
    "platform": "opencart",
    "store_id": "main"
  },
  "context": {
    "locale": "zh-TW",
    "permissions": ["inventory:read", "report:write"]
  },
  "data": {
    "period_days": 30,
    "items": []
  },
  "constraints": {
    "write_mode": "report_only",
    "do_not_create_purchase_order": true
  }
}
`

write_mode` is very important, it tells the system that this is just a report, not an automatic purchase.

Step 3: Node.js Example#

'use strict';

const payload = {
  intent: 'analyze_inventory_risk',
  source: { platform: 'woocommerce', store_id: 'demo' },
  context: {
    locale: 'zh-TW',
    permissions: ['inventory:read', 'report:write']
  },
  data: {
    period_days: 30,
    items: [
      {
        sku: 'BAG-18L-NAVY',
        name: '18L 防潑水通勤背包',
        current_stock: 84,
        sales_30d: 126,
        returns_30d: 3,
        supplier_lead_time_days: 21
      }
    ]
  },
  constraints: {
    write_mode: 'report_only',
    do_not_create_purchase_order: true
  }
};

const response = await fetch('https://gateway.example.com/ai-ready/tasks', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': `Bearer ${process.env.AI_READY_TOKEN}`
  },
  body: JSON.stringify(payload)
});

if (!response.ok) {
  throw new Error(`AI Ready task failed: ${response.status}`);
}

console.log(await response.json());

Production environments also require HMAC signatures, timeouts, retries, idempotency keys, and error logs.

Step 4: Verify output#

The expected output can be:

{
  "report": [
    {
      "sku": "BAG-18L-NAVY",
      "risk": "stockout",
      "reason": "30 天銷售 126 件，目前庫存 84 件，供應商交期 21 天。",
      "recommendation": "建議採購人員本週確認補貨。",
      "requires_review": true
    }
  ]
}

The system should check whether sku exists, whether risk is an allowed value, and whether recommendation contains automatic purchase instructions that should not be executed.

Step 5: Present in the background#

The report page is recommended to display:

• SKU and product name.

• Type of risk.

• AI suggestions.

• Data basis.

• Confidence level.

• Manual notes.

• Processed/Ignored status.

In this way, AI tools will become operational aids rather than black-box decision-making.

FAQ#

Is this tool really cross-platform?#

The core analysis logic can be cross-platform, but each platform still needs its own adapter to obtain data and write back reports. The focus of cross-platform is the common payload, not the exact same code.

Why not just let AI place purchase orders?#

Procurement involves suppliers, payment flows, inventory strategies and human judgment. The first phase should only generate suggested reports, and then consider higher automation once the company has established audits and permissions.

Do I need to send customer information to the model?#

unnecessary. Inventory level analysis usually uses aggregated sales and inventory data, and should not transmit names, addresses, emails, or payment information.

References#

• Node.js Fetch API, https://nodejs.org/api/globals.html#fetch
• WooCommerce REST API Documentation, https://woocommerce.github.io/woocommerce-rest-api-docs/
• Adobe Commerce Web APIs, https://developer.adobe.com/commerce/webapi/